Algorithm 2: Overall algorithm for Classification of five-classes recognition of ECG signals using residual and dense based CNN and LSVM techniques
Step 1:	Input: ECG signals dataset, 𝐷; Labels, 𝐿, Number of arrhythmia classes, 𝑛
Step 2:	Output: Evaluated performance metrics
Step 3:	Pre-process ECG signals to remove noise and make classes balance.
Step 4:	TrainedClassifier = Residual-Dense-Network (n)
Step 5:	Extract-features by Residual-based dense CNN model ()
Step 6:	TrainedClassifier by linear support vector machine (LSVM, n)
Step 7:	ClassifiedLabels = Predicted (TrainedClassifier)
Step 8:	PerformanceMetrics = EvaluatePerformanceMetrics(ClassifiedLabels, TestingLabels)
	return PerformanceMetrics